LoRA: Fine Tuning Without Regret

Описание: The document provides a detailed analysis of Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning (PEFT) method for large language models, comparing its performance against full fine-tuning (FullFT). It highlights the practical benefits of LoRA, such as multi-tenant serving, reduced memory requirements during training, and ease of transfer. The core of the research establishes the conditions under which LoRA can match the performance and sample efficiency of FullFT, finding that equivalent performance is achieved when LoRA is applied to all network layers, especially the MLP/MoE layers, and when the training dataset size does not exceed the LoRA adapter's capacity. Furthermore, the paper details experimental findings regarding the optimal learning rate ratio (approximately 10x higher for LoRA) and notes that LoRA is less tolerant of large batch sizes compared to FullFT, while it performs exceptionally well in reinforcement learning settings even with low ranks.

https://thinkingmachines.ai/blog/lora/

#ai #finetuning #agent #llm #largelanguagemodels #openai